Detection of metabolic signature for breast cancer (BC) has the potential to improve patient prognosis. This study identified potentially significant metabolites differentiating between breast cancer patients and healthy controls to help in diagnosis, grading, staging and determination of neoadjuvant status. Serum was collected from 152 pre-operative breast cancer (BC) patients and 155 healthy controls in this case-controlled study. Gas chromatography-mass spectrometry (GC-MS) was used to obtain metabolic profiles followed by chemometric analysis with the identification of significantly differentiated metabolites including 7 for diagnosis, 18 for grading, 23 for staging, 15 for stage III subcategory and 10 for neoadjuvant status (p-value < 0.05). Partial Least Square Discriminant Analysis (PLS-DA) model revealed a distinct separation between healthy controls and BC patients with a sensitivity of 96% and specificity of 100% on external validation. Models for grading, staging and neoadjuvant status were built with Decision Tree Algorithm with predictive accuracy of 71.5%, 71.3% and 79.8% respectively. Pathway analysis revealed increased glycolysis, lipogenesis, and production of volatile organic metabolites indicating the metabolic alterations in breast cancer.

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Objective:To record various clinicopathological characteristics of breast cancer (BC) in our population and to find an association between these characteristics and axillary nodal metastasis.Methods:This cross-sectional study included 150 BC patients from two tertiary care centers in Karachi from 15th February, 2013 to 31st March, 2015. Frequencies, percentages, and odds ratio were estimated to find out an association between various clinicopathological characteristics and lymph node status using SPSS version 20.Results:Approximately 75.4% patients had axillary lymph node metastasis (‘1-3’ LN = 34.4% and ‘>3’ LN = 44%). Menopausal status (p <0.013), tumor grades (‘II’ p <0.03; ‘III’ p <0.01), and stages (‘III’ p <0.002; ‘IV’ p <0.0001), tumor sizes (‘T2’ p <0.014; ‘T3’ p <0.002), perineural invasion (PNI) (p <0.007), lymphovascular invasion (LVI) (p <0.0001), and skin and nipple invasion (p <0.024) were significant predictors for ‘>3’ LN metastasis. Association of these variables with ‘1-3’ LN involvement was insignificant.Conclusion:Clinical spectrum of BC remains unchanged in 2016 with most of the patients presenting with high-grade, late-stage advanced disease. Moreover, clinicopathological variables, especially primary tumor size, tumor stage and lymphovascular invasion were significant predictors of >3 lymph node metastasis with high accuracy.

Breast cancer is a global health issue, and as the tumor burden increases, we need to come up with newer, better technologies which are convenient, cheap, rapid, sensitive with a high specificity. Technological advancements in the field of cancer biomarker has led to the development of techniques such as mass spectrometric analysis and microarray analysis in which genes, proteins and hundreds and thousands of metabolites can be identified with the emergence of genomics, proteomics and metabolomics. This research is focused on finding biomarkers for diagnosis, prognosis, staging, treatment response and targets for chemotherapy, generating a panel of markers which provide better clinical information compared to a single marker in the panel. This review briefly summarizes application of genomics and proteomics followed by key concepts and applications of metabolomics in breast cancer, with the conclusion that an integration of the three “OMIC” technologies may hold the key to future biomarker discovery.Sources of Data Study Selection:The information for this review was collected by searching the Google Scholar and PubMed database for English articles published in the period from 2002 to 2015. The search terms included “biomarkers in breast cancer” along with the following search terms: “genomics”, “proteomics”, “metabolomics”, “breast cancer”, “mass spectrometry”, “molecular markers” and “cancer biomarker”. We have endeavored to quote only the primary sources. Titles and abstracts of retrieved studies were assessed first followed by selection and retrieval of selected full text articles.

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